Amrita Nighojkar

525 total citations
27 papers, 389 citations indexed

About

Amrita Nighojkar is a scholar working on Water Science and Technology, Industrial and Manufacturing Engineering and Mechanical Engineering. According to data from OpenAlex, Amrita Nighojkar has authored 27 papers receiving a total of 389 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Water Science and Technology, 9 papers in Industrial and Manufacturing Engineering and 5 papers in Mechanical Engineering. Recurrent topics in Amrita Nighojkar's work include Adsorption and biosorption for pollutant removal (8 papers), Water Quality Monitoring and Analysis (4 papers) and Municipal Solid Waste Management (3 papers). Amrita Nighojkar is often cited by papers focused on Adsorption and biosorption for pollutant removal (8 papers), Water Quality Monitoring and Analysis (4 papers) and Municipal Solid Waste Management (3 papers). Amrita Nighojkar collaborates with scholars based in India, United States and Canada. Amrita Nighojkar's co-authors include Balasubramanian Kandasubramanian, Neelaambhigai Mayilswamy, Fuhar Dixit, Anand Plappally, Shruti Gupta, Winston O. Soboyejo, Karl Zimmermann, Mohan Edirisinghe, Senthilarasu Sundaram and Benoît Barbeau and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Hazardous Materials and International Journal of Hydrogen Energy.

In The Last Decade

Amrita Nighojkar

25 papers receiving 380 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Amrita Nighojkar India 11 119 95 75 72 69 27 389
Sai Praneeth United States 9 193 1.6× 67 0.7× 91 1.2× 86 1.2× 64 0.9× 20 561
Nan Cai China 9 104 0.9× 115 1.2× 57 0.8× 54 0.8× 103 1.5× 19 363
Mariam Khan Qatar 11 134 1.1× 104 1.1× 44 0.6× 54 0.8× 55 0.8× 16 380
Mingshuang Zhang China 9 123 1.0× 69 0.7× 55 0.7× 83 1.2× 100 1.4× 18 432
Longjie Ji China 12 116 1.0× 96 1.0× 54 0.7× 103 1.4× 105 1.5× 28 456
Hossein Ghiasinejad Iran 9 183 1.5× 81 0.9× 42 0.6× 82 1.1× 50 0.7× 12 417
Sara Kazemi Yazdi Malaysia 11 204 1.7× 75 0.8× 50 0.7× 48 0.7× 91 1.3× 22 496
Zhibo Hu China 7 82 0.7× 68 0.7× 85 1.1× 81 1.1× 68 1.0× 15 352
Aiguo Lin China 11 163 1.4× 45 0.5× 64 0.9× 58 0.8× 76 1.1× 19 354
Si Wan China 10 140 1.2× 60 0.6× 150 2.0× 71 1.0× 138 2.0× 24 416

Countries citing papers authored by Amrita Nighojkar

Since Specialization
Citations

This map shows the geographic impact of Amrita Nighojkar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Amrita Nighojkar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amrita Nighojkar more than expected).

Fields of papers citing papers by Amrita Nighojkar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amrita Nighojkar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Amrita Nighojkar. The network helps show where Amrita Nighojkar may publish in the future.

Co-authorship network of co-authors of Amrita Nighojkar

This figure shows the co-authorship network connecting the top 25 collaborators of Amrita Nighojkar. A scholar is included among the top collaborators of Amrita Nighojkar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Amrita Nighojkar. Amrita Nighojkar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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2.
Nighojkar, Amrita, et al.. (2025). Machine learning–assisted prediction of engineered carbon systems’ capacity to treat textile dyeing wastewater via adsorption technology. Environmental Monitoring and Assessment. 197(2). 223–223. 5 indexed citations
3.
Ramachandran, S., A. K. Gupta, Neelaambhigai Mayilswamy, Amrita Nighojkar, & Balasubramanian Kandasubramanian. (2025). Multifaceted applications of dye-saturated biochar: Agronomic amelioration, thermochemical valorization, and catalytic efficacy in advanced environmental remediation paradigms. SHILAP Revista de lepidopterología. 11. 100164–100164. 3 indexed citations
4.
Patra, Dipti, et al.. (2025). Predictive Models for Removing Heavy Metal Water Pollutants with Biochar: Exploring Neural Networks and Machine Learning. Procedia Computer Science. 258. 3135–3144. 1 indexed citations
5.
Gupta, A. K., S. Ramachandran, Neelaambhigai Mayilswamy, Amrita Nighojkar, & Balasubramanian Kandasubramanian. (2025). Dye-laden sludge-derived biochar for wastewater remediation: A review on pyrolytic engineering, adsorptive interactions, and environmental prospects. Sustainable Chemistry for the Environment. 11. 100271–100271.
6.
Nighojkar, Amrita, et al.. (2025). Artificial Intelligence in Maritime Anomaly Detection: A Decadal Bibliometric Analysis (2014–2024). Journal of The Institution of Engineers (India) Series C. 106(2). 665–689. 2 indexed citations
7.
Mayilswamy, Neelaambhigai, Amrita Nighojkar, Mohan Edirisinghe, Senthilarasu Sundaram, & Balasubramanian Kandasubramanian. (2023). Sludge-derived biochar: Physicochemical characteristics for environmental remediation. Applied Physics Reviews. 10(3). 31 indexed citations
8.
Nighojkar, Amrita, et al.. (2023). Exploring the future of 2D catalysts for clean and sustainable hydrogen production. International Journal of Hydrogen Energy. 48(74). 28679–28693. 38 indexed citations
9.
Nighojkar, Amrita, Minoo Naebe, Balasubramanian Kandasubramanian, et al.. (2023). Using machine learning to predict the efficiency of biochar in pesticide remediation. 1(1). 25 indexed citations
10.
Nighojkar, Amrita, et al.. (2023). Prediction of organophosphorus pesticide adsorption by biochar using ensemble learning algorithms. Environmental Monitoring and Assessment. 195(8). 984–984. 7 indexed citations
11.
Nighojkar, Amrita, et al.. (2023). Algal mediated intervention for the retrieval of emerging pollutants from aqueous media. Journal of Hazardous Materials. 455. 131568–131568. 30 indexed citations
12.
Nighojkar, Amrita, et al.. (2023). A review of the methods of harvesting atmospheric moisture. Environmental Science and Pollution Research. 31(7). 10395–10416. 8 indexed citations
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Nighojkar, Amrita, Anand Plappally, & Winston O. Soboyejo. (2023). Neural network models for simulating adsorptive eviction of metal contaminants from effluent streams using natural materials (NMs). Neural Computing and Applications. 35(8). 5751–5767. 9 indexed citations
15.
Nighojkar, Amrita, Karl Zimmermann, Mohamed Ateia, et al.. (2022). Application of neural network in metal adsorption using biomaterials (BMs): a review. Environmental Science Advances. 2(1). 11–38. 39 indexed citations
16.
Nighojkar, Amrita, Vikas Kumar Sangal, Fuhar Dixit, & Balasubramanian Kandasubramanian. (2022). Sustainable conversion of saturated adsorbents (SAs) from wastewater into value-added products: future prospects and challenges with toxic per- and poly-fluoroalkyl substances (PFAS). Environmental Science and Pollution Research. 29(52). 78207–78227. 20 indexed citations
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18.
Gupta, Sandeep, et al.. (2022). Clean drinking water solution for rural India: Portable sip-up. IOP Conference Series Earth and Environmental Science. 1084(1). 12008–12008. 2 indexed citations
19.
Nighojkar, Amrita, Anand Plappally, & Winston O. Soboyejo. (2021). Animated concept-in-context maps as a materials science learning resource in an online flipped classroom. MRS Advances. 6(13). 351–354. 2 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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